Overview

Dataset statistics

Number of variables10
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory83.7 B

Variable types

Numeric2
Text7
Categorical1

Dataset

Description샘플 데이터
Author한국해양과학기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=17da4da0-5db1-11ec-a202-0b2a0a987ad6

Alerts

데이터_건수 is highly overall correlated with 평균입도High correlation
평균입도 is highly overall correlated with 데이터_건수 and 1 other fieldsHigh correlation
퇴적상 is highly overall correlated with 평균입도High correlation
데이터_건수 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:12:04.490973
Analysis finished2023-12-10 11:12:06.461263
Duration1.97 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

데이터_건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-10T20:12:06.573441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityNot monotonic
2023-12-10T20:12:06.804358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
47 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
6 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%

관리번호
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:07.163431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9125
Min length2

Characters and Unicode

Total characters233
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowK1
2nd rowK12
3rd rowK13
4th rowK15
5th rowK16
ValueCountFrequency (%)
k1 1
 
1.2%
k12 1
 
1.2%
k65 1
 
1.2%
k64 1
 
1.2%
k63 1
 
1.2%
k7 1
 
1.2%
k62 1
 
1.2%
k61 1
 
1.2%
k60 1
 
1.2%
k66 1
 
1.2%
Other values (70) 70
87.5%
2023-12-10T20:12:07.662568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 80
34.3%
2 19
 
8.2%
1 18
 
7.7%
3 18
 
7.7%
5 18
 
7.7%
6 18
 
7.7%
7 18
 
7.7%
4 17
 
7.3%
8 11
 
4.7%
9 8
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 153
65.7%
Uppercase Letter 80
34.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19
12.4%
1 18
11.8%
3 18
11.8%
5 18
11.8%
6 18
11.8%
7 18
11.8%
4 17
11.1%
8 11
7.2%
9 8
5.2%
0 8
5.2%
Uppercase Letter
ValueCountFrequency (%)
K 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153
65.7%
Latin 80
34.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19
12.4%
1 18
11.8%
3 18
11.8%
5 18
11.8%
6 18
11.8%
7 18
11.8%
4 17
11.1%
8 11
7.2%
9 8
5.2%
0 8
5.2%
Latin
ValueCountFrequency (%)
K 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 80
34.3%
2 19
 
8.2%
1 18
 
7.7%
3 18
 
7.7%
5 18
 
7.7%
6 18
 
7.7%
7 18
 
7.7%
4 17
 
7.3%
8 11
 
4.7%
9 8
 
3.4%

위치(좌표계WGS84)위도_도
Text

Distinct77
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:07.945942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.9875
Min length10

Characters and Unicode

Total characters879
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)92.5%

Sample

1st rowN 37.60995°
2nd rowN 37.56177°
3rd rowN 37.59547°
4th rowN 37.57725°
5th rowN 37.57187°
ValueCountFrequency (%)
n 80
50.0%
37.57725° 2
 
1.2%
37.61392° 2
 
1.2%
37.58747° 2
 
1.2%
37.58853° 1
 
0.6%
37.61480° 1
 
0.6%
37.57212° 1
 
0.6%
37.57855° 1
 
0.6%
37.59647° 1
 
0.6%
37.60638° 1
 
0.6%
Other values (68) 68
42.5%
2023-12-10T20:12:08.393785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 140
15.9%
3 108
12.3%
5 107
12.2%
N 80
9.1%
80
9.1%
. 80
9.1%
° 80
9.1%
2 39
 
4.4%
6 37
 
4.2%
8 34
 
3.9%
Other values (4) 94
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 559
63.6%
Uppercase Letter 80
 
9.1%
Space Separator 80
 
9.1%
Other Punctuation 80
 
9.1%
Other Symbol 80
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 140
25.0%
3 108
19.3%
5 107
19.1%
2 39
 
7.0%
6 37
 
6.6%
8 34
 
6.1%
0 32
 
5.7%
9 26
 
4.7%
1 22
 
3.9%
4 14
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 799
90.9%
Latin 80
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
7 140
17.5%
3 108
13.5%
5 107
13.4%
80
10.0%
. 80
10.0%
° 80
10.0%
2 39
 
4.9%
6 37
 
4.6%
8 34
 
4.3%
0 32
 
4.0%
Other values (3) 62
7.8%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
90.9%
None 80
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 140
17.5%
3 108
13.5%
5 107
13.4%
N 80
10.0%
80
10.0%
. 80
10.0%
2 39
 
4.9%
6 37
 
4.6%
8 34
 
4.3%
0 32
 
4.0%
Other values (3) 62
7.8%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도
Text

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:08.738002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.9125
Min length9

Characters and Unicode

Total characters873
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)87.5%

Sample

1st rowE 126.5433°
2nd rowE 126.4851°
3rd rowE 126.4831°
4th rowE 126.4803°
5th rowE 126.4833°
ValueCountFrequency (%)
e 80
50.0%
126.3487° 2
 
1.2%
126.3667° 2
 
1.2%
126.3668° 2
 
1.2%
126.4324° 2
 
1.2%
126.3495° 2
 
1.2%
126.3491° 1
 
0.6%
126.5433° 1
 
0.6%
126.3654° 1
 
0.6%
126.5217° 1
 
0.6%
Other values (66) 66
41.2%
2023-12-10T20:12:09.201015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 115
13.2%
1 102
11.7%
2 99
11.3%
E 80
9.2%
80
9.2%
. 80
9.2%
° 80
9.2%
3 71
8.1%
4 62
7.1%
5 29
 
3.3%
Other values (4) 75
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 553
63.3%
Uppercase Letter 80
 
9.2%
Space Separator 80
 
9.2%
Other Punctuation 80
 
9.2%
Other Symbol 80
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 115
20.8%
1 102
18.4%
2 99
17.9%
3 71
12.8%
4 62
11.2%
5 29
 
5.2%
8 27
 
4.9%
9 25
 
4.5%
7 12
 
2.2%
0 11
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 793
90.8%
Latin 80
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 115
14.5%
1 102
12.9%
2 99
12.5%
80
10.1%
. 80
10.1%
° 80
10.1%
3 71
9.0%
4 62
7.8%
5 29
 
3.7%
8 27
 
3.4%
Other values (3) 48
6.1%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 793
90.8%
None 80
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 115
14.5%
1 102
12.9%
2 99
12.5%
E 80
10.1%
80
10.1%
. 80
10.1%
3 71
9.0%
4 62
7.8%
5 29
 
3.7%
8 27
 
3.4%
Other values (3) 48
6.1%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)위도_도분
Text

Distinct73
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:09.559531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1120
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)82.5%

Sample

1st rowN 37° 36.597'
2nd rowN 37° 33.779'
3rd rowN 37° 35.617'
4th rowN 37° 34.635'
5th rowN 37° 34.312'
ValueCountFrequency (%)
n 80
33.3%
37° 80
33.3%
33.779 2
 
0.8%
33.451 2
 
0.8%
33.182 2
 
0.8%
35.302 2
 
0.8%
35.248 2
 
0.8%
35.788 2
 
0.8%
34.635 2
 
0.8%
36.888 1
 
0.4%
Other values (65) 65
27.1%
2023-12-10T20:12:10.098727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
21.4%
3 215
19.2%
7 119
10.6%
N 80
 
7.1%
° 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
4 48
 
4.3%
5 33
 
2.9%
2 31
 
2.8%
Other values (5) 114
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 560
50.0%
Space Separator 240
21.4%
Other Punctuation 160
 
14.3%
Uppercase Letter 80
 
7.1%
Other Symbol 80
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 215
38.4%
7 119
21.2%
4 48
 
8.6%
5 33
 
5.9%
2 31
 
5.5%
8 27
 
4.8%
6 26
 
4.6%
1 25
 
4.5%
0 23
 
4.1%
9 13
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 80
50.0%
' 80
50.0%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1040
92.9%
Latin 80
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
240
23.1%
3 215
20.7%
7 119
11.4%
° 80
 
7.7%
. 80
 
7.7%
' 80
 
7.7%
4 48
 
4.6%
5 33
 
3.2%
2 31
 
3.0%
8 27
 
2.6%
Other values (4) 87
 
8.4%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1040
92.9%
None 80
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
23.1%
3 215
20.7%
7 119
11.4%
N 80
 
7.7%
. 80
 
7.7%
' 80
 
7.7%
4 48
 
4.6%
5 33
 
3.2%
2 31
 
3.0%
8 27
 
2.6%
Other values (4) 87
 
8.4%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도분
Text

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:10.405236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1200
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)85.0%

Sample

1st rowE 126° 32.600'
2nd rowE 126° 29.785'
3rd rowE 126° 28.988'
4th rowE 126° 28.817'
5th rowE 126° 28.996'
ValueCountFrequency (%)
e 80
33.3%
126° 80
33.3%
29.785 2
 
0.8%
20.968 2
 
0.8%
25.943 2
 
0.8%
22.003 2
 
0.8%
22.007 2
 
0.8%
20.011 2
 
0.8%
21.972 1
 
0.4%
22.948 1
 
0.4%
Other values (66) 66
27.5%
2023-12-10T20:12:10.937737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
20.0%
2 187
15.6%
1 118
9.8%
6 99
8.2%
E 80
 
6.7%
° 80
 
6.7%
. 80
 
6.7%
' 80
 
6.7%
0 58
 
4.8%
9 54
 
4.5%
Other values (5) 124
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 640
53.3%
Space Separator 240
 
20.0%
Other Punctuation 160
 
13.3%
Uppercase Letter 80
 
6.7%
Other Symbol 80
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 187
29.2%
1 118
18.4%
6 99
15.5%
0 58
 
9.1%
9 54
 
8.4%
8 28
 
4.4%
3 28
 
4.4%
7 25
 
3.9%
5 23
 
3.6%
4 20
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 80
50.0%
' 80
50.0%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
93.3%
Latin 80
 
6.7%

Most frequent character per script

Common
ValueCountFrequency (%)
240
21.4%
2 187
16.7%
1 118
10.5%
6 99
8.8%
° 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
0 58
 
5.2%
9 54
 
4.8%
8 28
 
2.5%
Other values (4) 96
 
8.6%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
93.3%
None 80
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
21.4%
2 187
16.7%
1 118
10.5%
6 99
8.8%
E 80
 
7.1%
. 80
 
7.1%
' 80
 
7.1%
0 58
 
5.2%
9 54
 
4.8%
8 28
 
2.5%
Other values (4) 96
 
8.6%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)위도_도분초
Text

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:11.252134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.9875
Min length17

Characters and Unicode

Total characters1439
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)88.8%

Sample

1st rowN 37° 36' 35.8199"
2nd rowN 37° 33' 42.3720"
3rd rowN 37° 35' 43.6919"
4th rowN 37° 34' 38.0999"
5th rowN 37° 34' 38.0999"
ValueCountFrequency (%)
n 80
25.0%
37° 80
25.0%
34 29
 
9.1%
33 22
 
6.9%
35 15
 
4.7%
36 10
 
3.1%
38.0999 3
 
0.9%
37 3
 
0.9%
14.8920 2
 
0.6%
47.0279 2
 
0.6%
Other values (73) 74
23.1%
2023-12-10T20:12:11.741206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
16.7%
3 214
14.9%
7 119
 
8.3%
9 90
 
6.3%
N 80
 
5.6%
° 80
 
5.6%
' 80
 
5.6%
. 80
 
5.6%
" 80
 
5.6%
4 75
 
5.2%
Other values (6) 301
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 799
55.5%
Space Separator 240
 
16.7%
Other Punctuation 240
 
16.7%
Uppercase Letter 80
 
5.6%
Other Symbol 80
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 214
26.8%
7 119
14.9%
9 90
11.3%
4 75
 
9.4%
0 71
 
8.9%
2 63
 
7.9%
1 62
 
7.8%
5 46
 
5.8%
8 34
 
4.3%
6 25
 
3.1%
Other Punctuation
ValueCountFrequency (%)
' 80
33.3%
. 80
33.3%
" 80
33.3%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
94.4%
Latin 80
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
240
17.7%
3 214
15.7%
7 119
8.8%
9 90
 
6.6%
° 80
 
5.9%
' 80
 
5.9%
. 80
 
5.9%
" 80
 
5.9%
4 75
 
5.5%
0 71
 
5.2%
Other values (5) 230
16.9%
Latin
ValueCountFrequency (%)
N 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
94.4%
None 80
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
17.7%
3 214
15.7%
7 119
8.8%
9 90
 
6.6%
N 80
 
5.9%
' 80
 
5.9%
. 80
 
5.9%
" 80
 
5.9%
4 75
 
5.5%
0 71
 
5.2%
Other values (5) 230
16.9%
None
ValueCountFrequency (%)
° 80
100.0%

위치(좌표계WGS84)경도_도분초
Text

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-10T20:12:12.059376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.6125
Min length18

Characters and Unicode

Total characters1489
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)85.0%

Sample

1st rowE 126° 32' 35.8800"
2nd rowE 126° 29' 6.3600"
3rd rowE 126° 28' 59.1599"
4th rowE 126° 28' 49.0799"
5th rowE 126° 28' 49.0799"
ValueCountFrequency (%)
e 80
25.0%
126° 80
25.0%
20 10
 
3.1%
22 9
 
2.8%
23 9
 
2.8%
27 7
 
2.2%
19 7
 
2.2%
25 7
 
2.2%
21 6
 
1.9%
26 5
 
1.6%
Other values (72) 100
31.2%
2023-12-10T20:12:12.518669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
16.1%
2 182
12.2%
0 132
8.9%
1 127
8.5%
6 110
 
7.4%
9 99
 
6.6%
E 80
 
5.4%
° 80
 
5.4%
' 80
 
5.4%
. 80
 
5.4%
Other values (6) 279
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 849
57.0%
Space Separator 240
 
16.1%
Other Punctuation 240
 
16.1%
Uppercase Letter 80
 
5.4%
Other Symbol 80
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 182
21.4%
0 132
15.5%
1 127
15.0%
6 110
13.0%
9 99
11.7%
5 63
 
7.4%
3 40
 
4.7%
4 37
 
4.4%
7 31
 
3.7%
8 28
 
3.3%
Other Punctuation
ValueCountFrequency (%)
' 80
33.3%
. 80
33.3%
" 80
33.3%
Space Separator
ValueCountFrequency (%)
240
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 80
100.0%
Other Symbol
ValueCountFrequency (%)
° 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1409
94.6%
Latin 80
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
240
17.0%
2 182
12.9%
0 132
9.4%
1 127
9.0%
6 110
7.8%
9 99
7.0%
° 80
 
5.7%
' 80
 
5.7%
. 80
 
5.7%
" 80
 
5.7%
Other values (5) 199
14.1%
Latin
ValueCountFrequency (%)
E 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1409
94.6%
None 80
 
5.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
17.0%
2 182
12.9%
0 132
9.4%
1 127
9.0%
6 110
7.8%
9 99
7.0%
E 80
 
5.7%
' 80
 
5.7%
. 80
 
5.7%
" 80
 
5.7%
Other values (5) 199
14.1%
None
ValueCountFrequency (%)
° 80
100.0%

평균입도
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.279125
Minimum0.9
Maximum7.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-10T20:12:12.721150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.176
Q14.41
median5.535
Q36.255
95-th percentile7.341
Maximum7.87
Range6.97
Interquartile range (IQR)1.845

Descriptive statistics

Standard deviation1.5639816
Coefficient of variation (CV)0.29625773
Kurtosis0.11697338
Mean5.279125
Median Absolute Deviation (MAD)0.975
Skewness-0.77953848
Sum422.33
Variance2.4460385
MonotonicityNot monotonic
2023-12-10T20:12:13.169890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.34 2
 
2.5%
6.16 2
 
2.5%
5.35 2
 
2.5%
6.8 2
 
2.5%
5.85 2
 
2.5%
2.42 2
 
2.5%
5.08 1
 
1.2%
5.21 1
 
1.2%
5.38 1
 
1.2%
5.53 1
 
1.2%
Other values (64) 64
80.0%
ValueCountFrequency (%)
0.9 1
1.2%
1.75 1
1.2%
1.89 1
1.2%
1.91 1
1.2%
2.19 1
1.2%
2.42 2
2.5%
2.46 1
1.2%
3.16 1
1.2%
3.19 1
1.2%
3.2 1
1.2%
ValueCountFrequency (%)
7.87 1
1.2%
7.71 1
1.2%
7.5 1
1.2%
7.36 1
1.2%
7.34 2
2.5%
7.23 1
1.2%
7.21 1
1.2%
7.07 1
1.2%
6.94 1
1.2%
6.8 2
2.5%

퇴적상
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
sM
30 
M
23 
mS
15 
S
5 
(g)S
 
3
Other values (4)
4 

Length

Max length5
Median length2
Mean length1.7875
Min length1

Unique

Unique4 ?
Unique (%)5.0%

Sample

1st rowM
2nd rowsM
3rd rowM
4th rowM
5th rowsM

Common Values

ValueCountFrequency (%)
sM 30
37.5%
M 23
28.7%
mS 15
18.8%
S 5
 
6.2%
(g)S 3
 
3.8%
(g)M 1
 
1.2%
(g)sM 1
 
1.2%
gM 1
 
1.2%
sG 1
 
1.2%

Length

2023-12-10T20:12:13.326728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:13.481050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sm 30
37.5%
m 23
28.7%
ms 15
18.8%
s 5
 
6.2%
g)s 3
 
3.8%
g)m 1
 
1.2%
g)sm 1
 
1.2%
gm 1
 
1.2%
sg 1
 
1.2%

Interactions

2023-12-10T20:12:05.860058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:05.612389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:05.974181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:05.728318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:12:13.594338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초평균입도퇴적상
데이터_건수1.0001.0000.7960.9740.7960.9140.9220.9790.5720.543
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(좌표계WGS84)위도_도0.7961.0001.0000.9950.9990.9941.0000.9960.8190.952
위치(좌표계WGS84)경도_도0.9741.0000.9951.0000.9640.9980.9941.0000.8780.967
위치(좌표계WGS84)위도_도분0.7961.0000.9990.9641.0000.9740.9950.9640.0000.121
위치(좌표계WGS84)경도_도분0.9141.0000.9940.9980.9741.0000.9840.9990.9450.977
위치(좌표계WGS84)위도_도분초0.9221.0001.0000.9940.9950.9841.0000.9940.9280.968
위치(좌표계WGS84)경도_도분초0.9791.0000.9961.0000.9640.9990.9941.0000.9180.967
평균입도0.5721.0000.8190.8780.0000.9450.9280.9181.0000.850
퇴적상0.5431.0000.9520.9670.1210.9770.9680.9670.8501.000
2023-12-10T20:12:13.746988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수평균입도퇴적상
데이터_건수1.000-0.7200.279
평균입도-0.7201.0000.599
퇴적상0.2790.5991.000

Missing values

2023-12-10T20:12:06.160925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:12:06.374354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초평균입도퇴적상
01K1N 37.60995°E 126.5433°N 37° 36.597'E 126° 32.600'N 37° 36' 35.8199"E 126° 32' 35.8800"7.23M
110K12N 37.56177°E 126.4851°N 37° 33.779'E 126° 29.785'N 37° 33' 42.3720"E 126° 29' 6.3600"5.27sM
211K13N 37.59547°E 126.4831°N 37° 35.617'E 126° 28.988'N 37° 35' 43.6919"E 126° 28' 59.1599"7.87M
312K15N 37.57725°E 126.4803°N 37° 34.635'E 126° 28.817'N 37° 34' 38.0999"E 126° 28' 49.0799"6.19M
413K16N 37.57187°E 126.4833°N 37° 34.312'E 126° 28.996'N 37° 34' 38.0999"E 126° 28' 49.0799"5.93sM
514K17N 37.56298°E 126.4819°N 37° 33.779'E 126° 29.105'N 37° 33' 46.7280"E 126° 28' 54.8399"5.63sM
615K18N 37.55907°E 126.4842°N 37° 33.182'E 126° 29.050'N 37° 33' 32.6519"E 126° 29' 3.1200"6.59M
716K19N 37.59005°E 126.4669°N 37° 35.403'E 126° 28.013'N 37° 35' 24.1799"E 126° 28' 0.8399"6.21M
817K20N 37.58012°E 126.466°N 37° 34.807'E 126° 27.961'N 37° 34' 48.4320"E 126° 27' 57.5999"6.78sM
918K21N 37.57320°E 126.4664°N 37° 34.392'E 126° 27.984'N 37° 34' 23.5199"E 126° 27' 59.0399"6.69M
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초평균입도퇴적상
7073K76N 37.58127°E 126.3382°N 37° 35.302'E 126° 20.011'N 37° 34' 52.5720"E 126° 20' 1.5200"4.58mS
7174K77N 37.57725°E 126.3304°N 37° 34.635'E 126° 19.826'N 37° 34' 38.0999"E 126° 19' 49.4399"3.16mS
7275K78N 37.57025°E 126.3292°N 37° 34.215'E 126° 19.752'N 37° 34' 12.9000"E 126° 19' 45.1200"3.19mS
7376K79N 37.56295°E 126.3353°N 37° 33.777'E 126° 20.120'N 37° 33' 46.6200"E 126° 20' 7.0800"3.94mS
7477K80N 37.58747°E 126.3204°N 37° 35.248'E 126° 19.222'N 37° 35' 14.8920"E 126° 19' 13.4400"2.42S
7578K81N 37.57865°E 126.3194°N 37° 34.719'E 126° 19.162'N 37° 34' 43.1400"E 126° 19' 9.8400"2.46S
7679K82N 37.57203°E 126.3187°N 37° 34.322'E 126° 19.124'N 37° 34' 19.3079"E 126° 19' 7.3200"1.89S
778K10N 37.57748°E 126.4954°N 37° 34.649'E 126° 29.726'N 37° 34' 38.9280"E 126° 29' 43.4400"6.94M
7880K83N 37.56270°E 126.3192°N 37° 33.762'E 126° 19.154'N 37° 33' 45.7199"E 126° 19' 9.1199"1.75S
799K11N 37.57218°E 126.4964°N 37° 34.331'E 126° 29.785'N 37° 34' 19.8480"E 126° 29' 47.0399"7.07M